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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 Hits 1 – 20 of 29 1 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Ponti, Edoardo Maria; O’Horan, Helen; Berzak, Yevgeni... In: ISSN: 0891-2017 ; EISSN: 1530-9312 ; Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-02425462 ; Computational Linguistics, Massachusetts Institute of Technology Press (MIT Press), 2019, 45 (3), pp.559-601. ⟨10.1162/coli_a_00357⟩ ; https://www.mitpressjournals.org/doi/abs/10.1162/coli_a_00357 (2019) BASE Show details 2 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing ... Ponti, Edoardo; O'Horan, Helen; Berzak, Yevgeni. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 3 Show Some Love to Your n-grams: A Bit of Progress and Stronger n-gram Language Modeling Baselines ... Shareghi, Ehsan; Gerz, Daniela; Vulic, Ivan. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 4 Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity ... Lauscher, Anne; Vulić, Ivan; Ponti, Edoardo Maria. - : arXiv, 2019 BASE Show details 5 Do We Really Need Fully Unsupervised Cross-Lingual Embeddings? ... Vulić, Ivan; Glavaš, Goran; Reichart, Roi. - : arXiv, 2019 BASE Show details 6 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019 BASE Show details 7 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Figshare, 2019 BASE Show details 8 Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation ... Liu, Qianchu; McCarthy, D; Vulić, I. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 9 A neural classification method for supporting the creation of BioVerbNet ... Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 10 Second-order contexts from lexical substitutes for few-shot learning of word representations ... Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 11 A Neural Classification Method for Supporting the Creation of BioVerbNet ... Chiu, Hon Wing; Majewska, Olga; Pyysalo, Sampo. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 12 Enhancing biomedical word embeddings by retrofitting to verb clusters ... Chiu, B; Baker, Simon; Palmer, M. - : Apollo - University of Cambridge Repository, 2019 BASE Show details 13 Crowdsourcing and Aggregating Nested Markable Annotations Madge, Chris; Yu, Juntao; Chamberlain, Jon. - : Association for Computational Linguistics, 2019 BASE Show details 14 A Neural Classification Method for Supporting the Creation of BioVerbNet Chiu, Hon Wing; Majewska, Olga; Pyysalo, Sampo. - : BioMed Central, 2019. : https://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-018-0193-x, 2019. : Journal of Biomedical Semantics, 2019 BASE Show details 15 Second-order contexts from lexical substitutes for few-shot learning of word representations Liu, Qianchu; McCarthy, D; Korhonen, Anna-Leena. - : *SEM@NAACL-HLT 2019 - 8th Joint Conference on Lexical and Computational Semantics, 2019 BASE Show details 16 Investigating cross-lingual alignment methods for contextualized embeddings with Token-level evaluation Liu, Qianchu; McCarthy, D; Vulić, I. - : CoNLL 2019 - 23rd Conference on Computational Natural Language Learning, Proceedings of the Conference, 2019 BASE Show details 17 A neural classification method for supporting the creation of BioVerbNet Chiu, Billy; Majewska, Olga; Pyysalo, Sampo. - : BioMed Central, 2019 BASE Show details 18 Enhancing biomedical word embeddings by retrofitting to verb clusters Chiu, B; Baker, Simon; Palmer, M. - : Association for Computational Linguistics, 2019. : https://www.aclweb.org/anthology/W19-50, 2019. : BioNLP 2019 - SIGBioMed Workshop on Biomedical Natural Language Processing, Proceedings of the 18th BioNLP Workshop and Shared Task, 2019 BASE Show details 19 Bayesian learning for neural dependency parsing Shareghi, E; Li, Y; Zhu, Y. - : NAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference, 2019 BASE Show details 20 Modeling Language Variation and Universals: A Survey on Typological Linguistics for Natural Language Processing Reichart, Roi; Shutova, Ekaterina; Korhonen, Anna-Leena; Poibeau, Thierry; Berzak, Yevgeni; Vulic, Ivan; O'Horan, Helen; Ponti, Edoardo. - : MIT Press - Journals, 2019. : COMPUTATIONAL LINGUISTICS, 2019 Abstract: Linguistic typology aims to capture structural and semantic variation across the world’s languages. A large-scale typology could provide excellent guidance for multilingual Natural Language Processing (NLP), particularly for languages that suffer from the lack of human labeled resources. We present an extensive literature survey on the use of typological information in the development of NLP techniques. Our survey demonstrates that to date, the use of information in existing typological databases has resulted in consistent but modest improvements in system performance. We show that this is due to both intrinsic limitations of databases (in terms of coverage and feature granularity) and under-utilization of the typological features included in them. We advocate for a new approach that adapts the broad and discrete nature of typological categories to the contextual and continuous nature of machine learning algorithms used in contemporary NLP. In particular, we suggest that such an approach could be facilitated by recent developments in data-driven induction of typological knowledge. URL: https://doi.org/10.17863/CAM.43731 https://www.repository.cam.ac.uk/handle/1810/296683 BASE Hide details Page: 1 2

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